Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations14
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 KiB
Average record size in memory509.7 B

Variable types

Numeric11
Categorical4
Text4

Alerts

User ID has constant value "YcDFSO4ZukTJnnFMgRNVwZTE4j42" Constant
Marks for Correct Answer has constant value "4" Constant
Negative Marks has constant value "1" Constant
Better Than is highly overall correlated with Correct Answers and 9 other fieldsHigh correlation
Correct Answers is highly overall correlated with Better Than and 3 other fieldsHigh correlation
Difficulty Level is highly overall correlated with Better Than and 5 other fieldsHigh correlation
Difficulty Score is highly overall correlated with Better Than and 9 other fieldsHigh correlation
Incorrect Answers is highly overall correlated with Better Than and 6 other fieldsHigh correlation
Negative Score is highly overall correlated with Better Than and 6 other fieldsHigh correlation
Percentage Score is highly overall correlated with Better Than and 9 other fieldsHigh correlation
Questions Count is highly overall correlated with Better Than and 7 other fieldsHigh correlation
Quiz ID is highly overall correlated with Better Than and 7 other fieldsHigh correlation
Score is highly overall correlated with Better Than and 3 other fieldsHigh correlation
Total Questions is highly overall correlated with Better Than and 7 other fieldsHigh correlation
Submission ID has unique values Unique
Difficulty Score has unique values Unique
Negative Score has 3 (21.4%) zeros Zeros
Incorrect Answers has 3 (21.4%) zeros Zeros

Reproduction

Analysis started2025-01-27 08:05:57.596266
Analysis finished2025-01-27 08:06:01.500924
Duration3.9 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Submission ID
Real number (ℝ)

Unique 

Distinct14
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean312368.43
Minimum195808
Maximum336497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size244.0 B
2025-01-27T13:36:01.522437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum195808
5-th percentile236085.9
Q1316613.25
median324964
Q3332307.5
95-th percentile336465.15
Maximum336497
Range140689
Interquartile range (IQR)15694.25

Descriptive statistics

Standard deviation38889.14
Coefficient of variation (CV)0.12449766
Kurtosis6.580022
Mean312368.43
Median Absolute Deviation (MAD)8322
Skewness-2.5705141
Sum4373158
Variance1.5123652 × 109
MonotonicityStrictly decreasing
2025-01-27T13:36:01.550695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
336497 1
 
7.1%
336448 1
 
7.1%
333330 1
 
7.1%
333242 1
 
7.1%
329504 1
 
7.1%
328488 1
 
7.1%
328414 1
 
7.1%
321514 1
 
7.1%
320963 1
 
7.1%
320916 1
 
7.1%
Other values (4) 4
28.6%
ValueCountFrequency (%)
195808 1
7.1%
257774 1
7.1%
315081 1
7.1%
315179 1
7.1%
320916 1
7.1%
320963 1
7.1%
321514 1
7.1%
328414 1
7.1%
328488 1
7.1%
329504 1
7.1%
ValueCountFrequency (%)
336497 1
7.1%
336448 1
7.1%
333330 1
7.1%
333242 1
7.1%
329504 1
7.1%
328488 1
7.1%
328414 1
7.1%
321514 1
7.1%
320963 1
7.1%
320916 1
7.1%

Quiz ID
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.5
Minimum6
Maximum58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size244.0 B
2025-01-27T13:36:01.576946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6
Q118
median24.5
Q351
95-th percentile57.35
Maximum58
Range52
Interquartile range (IQR)33

Descriptive statistics

Standard deviation20.346045
Coefficient of variation (CV)0.64590618
Kurtosis-1.8240198
Mean31.5
Median Absolute Deviation (MAD)18.5
Skewness0.073361363
Sum441
Variance413.96154
MonotonicityNot monotonic
2025-01-27T13:36:01.600428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
51 3
21.4%
6 3
21.4%
18 2
14.3%
57 1
 
7.1%
20 1
 
7.1%
24 1
 
7.1%
25 1
 
7.1%
58 1
 
7.1%
50 1
 
7.1%
ValueCountFrequency (%)
6 3
21.4%
18 2
14.3%
20 1
 
7.1%
24 1
 
7.1%
25 1
 
7.1%
50 1
 
7.1%
51 3
21.4%
57 1
 
7.1%
58 1
 
7.1%
ValueCountFrequency (%)
58 1
 
7.1%
57 1
 
7.1%
51 3
21.4%
50 1
 
7.1%
25 1
 
7.1%
24 1
 
7.1%
20 1
 
7.1%
18 2
14.3%
6 3
21.4%

User ID
Categorical

Constant 

Distinct1
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
YcDFSO4ZukTJnnFMgRNVwZTE4j42
14 

Length

Max length28
Median length28
Mean length28
Min length28

Characters and Unicode

Total characters392
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYcDFSO4ZukTJnnFMgRNVwZTE4j42
2nd rowYcDFSO4ZukTJnnFMgRNVwZTE4j42
3rd rowYcDFSO4ZukTJnnFMgRNVwZTE4j42
4th rowYcDFSO4ZukTJnnFMgRNVwZTE4j42
5th rowYcDFSO4ZukTJnnFMgRNVwZTE4j42

Common Values

ValueCountFrequency (%)
YcDFSO4ZukTJnnFMgRNVwZTE4j42 14
100.0%

Length

2025-01-27T13:36:01.628943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T13:36:01.653744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ycdfso4zuktjnnfmgrnvwzte4j42 14
100.0%

Most occurring characters

ValueCountFrequency (%)
4 42
 
10.7%
F 28
 
7.1%
Z 28
 
7.1%
T 28
 
7.1%
n 28
 
7.1%
Y 14
 
3.6%
g 14
 
3.6%
j 14
 
3.6%
E 14
 
3.6%
w 14
 
3.6%
Other values (12) 168
42.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 392
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 42
 
10.7%
F 28
 
7.1%
Z 28
 
7.1%
T 28
 
7.1%
n 28
 
7.1%
Y 14
 
3.6%
g 14
 
3.6%
j 14
 
3.6%
E 14
 
3.6%
w 14
 
3.6%
Other values (12) 168
42.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 392
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 42
 
10.7%
F 28
 
7.1%
Z 28
 
7.1%
T 28
 
7.1%
n 28
 
7.1%
Y 14
 
3.6%
g 14
 
3.6%
j 14
 
3.6%
E 14
 
3.6%
w 14
 
3.6%
Other values (12) 168
42.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 392
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 42
 
10.7%
F 28
 
7.1%
Z 28
 
7.1%
T 28
 
7.1%
n 28
 
7.1%
Y 14
 
3.6%
g 14
 
3.6%
j 14
 
3.6%
E 14
 
3.6%
w 14
 
3.6%
Other values (12) 168
42.9%

Score
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.285714
Minimum12
Maximum116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size244.0 B
2025-01-27T13:36:01.672319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile19.8
Q136
median46
Q388
95-th percentile113.4
Maximum116
Range104
Interquartile range (IQR)52

Descriptive statistics

Standard deviation34.67526
Coefficient of variation (CV)0.57518203
Kurtosis-1.1659932
Mean60.285714
Median Absolute Deviation (MAD)20
Skewness0.50608374
Sum844
Variance1202.3736
MonotonicityNot monotonic
2025-01-27T13:36:01.698183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
36 3
21.4%
40 2
14.3%
108 1
 
7.1%
92 1
 
7.1%
116 1
 
7.1%
12 1
 
7.1%
76 1
 
7.1%
112 1
 
7.1%
64 1
 
7.1%
52 1
 
7.1%
ValueCountFrequency (%)
12 1
 
7.1%
24 1
 
7.1%
36 3
21.4%
40 2
14.3%
52 1
 
7.1%
64 1
 
7.1%
76 1
 
7.1%
92 1
 
7.1%
108 1
 
7.1%
112 1
 
7.1%
ValueCountFrequency (%)
116 1
 
7.1%
112 1
 
7.1%
108 1
 
7.1%
92 1
 
7.1%
76 1
 
7.1%
64 1
 
7.1%
52 1
 
7.1%
40 2
14.3%
36 3
21.4%
24 1
 
7.1%
Distinct11
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Memory size863.0 B
2025-01-27T13:36:01.740555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.2142857
Min length3

Characters and Unicode

Total characters45
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)64.3%

Sample

1st row90%
2nd row100%
3rd row96%
4th row90%
5th row31%
ValueCountFrequency (%)
100 3
21.4%
90 2
14.3%
96 1
 
7.1%
31 1
 
7.1%
38 1
 
7.1%
50 1
 
7.1%
30 1
 
7.1%
93 1
 
7.1%
84 1
 
7.1%
43 1
 
7.1%
2025-01-27T13:36:01.812123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 14
31.1%
0 10
22.2%
3 5
 
11.1%
1 4
 
8.9%
9 4
 
8.9%
6 3
 
6.7%
8 2
 
4.4%
4 2
 
4.4%
5 1
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
% 14
31.1%
0 10
22.2%
3 5
 
11.1%
1 4
 
8.9%
9 4
 
8.9%
6 3
 
6.7%
8 2
 
4.4%
4 2
 
4.4%
5 1
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
% 14
31.1%
0 10
22.2%
3 5
 
11.1%
1 4
 
8.9%
9 4
 
8.9%
6 3
 
6.7%
8 2
 
4.4%
4 2
 
4.4%
5 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
% 14
31.1%
0 10
22.2%
3 5
 
11.1%
1 4
 
8.9%
9 4
 
8.9%
6 3
 
6.7%
8 2
 
4.4%
4 2
 
4.4%
5 1
 
2.2%

Negative Score
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8571429
Minimum0
Maximum20
Zeros3
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size244.0 B
2025-01-27T13:36:01.838543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38.5
95-th percentile18.05
Maximum20
Range20
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation6.9487923
Coefficient of variation (CV)1.1863792
Kurtosis-0.10514681
Mean5.8571429
Median Absolute Deviation (MAD)3
Skewness1.157895
Sum82
Variance48.285714
MonotonicityNot monotonic
2025-01-27T13:36:01.865897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 3
21.4%
0 3
21.4%
1 2
14.3%
20 1
 
7.1%
16 1
 
7.1%
9 1
 
7.1%
7 1
 
7.1%
2 1
 
7.1%
17 1
 
7.1%
ValueCountFrequency (%)
0 3
21.4%
1 2
14.3%
2 1
 
7.1%
3 3
21.4%
7 1
 
7.1%
9 1
 
7.1%
16 1
 
7.1%
17 1
 
7.1%
20 1
 
7.1%
ValueCountFrequency (%)
20 1
 
7.1%
17 1
 
7.1%
16 1
 
7.1%
9 1
 
7.1%
7 1
 
7.1%
3 3
21.4%
2 1
 
7.1%
1 2
14.3%
0 3
21.4%

Correct Answers
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.071429
Minimum3
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size244.0 B
2025-01-27T13:36:01.891862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.95
Q19
median11.5
Q322
95-th percentile28.35
Maximum29
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.6688149
Coefficient of variation (CV)0.57518203
Kurtosis-1.1659932
Mean15.071429
Median Absolute Deviation (MAD)5
Skewness0.50608374
Sum211
Variance75.148352
MonotonicityNot monotonic
2025-01-27T13:36:01.916583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
9 3
21.4%
10 2
14.3%
27 1
 
7.1%
23 1
 
7.1%
29 1
 
7.1%
3 1
 
7.1%
19 1
 
7.1%
28 1
 
7.1%
16 1
 
7.1%
13 1
 
7.1%
ValueCountFrequency (%)
3 1
 
7.1%
6 1
 
7.1%
9 3
21.4%
10 2
14.3%
13 1
 
7.1%
16 1
 
7.1%
19 1
 
7.1%
23 1
 
7.1%
27 1
 
7.1%
28 1
 
7.1%
ValueCountFrequency (%)
29 1
 
7.1%
28 1
 
7.1%
27 1
 
7.1%
23 1
 
7.1%
19 1
 
7.1%
16 1
 
7.1%
13 1
 
7.1%
10 2
14.3%
9 3
21.4%
6 1
 
7.1%

Incorrect Answers
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8571429
Minimum0
Maximum20
Zeros3
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size244.0 B
2025-01-27T13:36:01.940723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38.5
95-th percentile18.05
Maximum20
Range20
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation6.9487923
Coefficient of variation (CV)1.1863792
Kurtosis-0.10514681
Mean5.8571429
Median Absolute Deviation (MAD)3
Skewness1.157895
Sum82
Variance48.285714
MonotonicityNot monotonic
2025-01-27T13:36:01.967154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 3
21.4%
0 3
21.4%
1 2
14.3%
20 1
 
7.1%
16 1
 
7.1%
9 1
 
7.1%
7 1
 
7.1%
2 1
 
7.1%
17 1
 
7.1%
ValueCountFrequency (%)
0 3
21.4%
1 2
14.3%
2 1
 
7.1%
3 3
21.4%
7 1
 
7.1%
9 1
 
7.1%
16 1
 
7.1%
17 1
 
7.1%
20 1
 
7.1%
ValueCountFrequency (%)
20 1
 
7.1%
17 1
 
7.1%
16 1
 
7.1%
9 1
 
7.1%
7 1
 
7.1%
3 3
21.4%
2 1
 
7.1%
1 2
14.3%
0 3
21.4%

Total Questions
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.5
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size244.0 B
2025-01-27T13:36:01.992849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile21.3
Q123
median48
Q397.25
95-th percentile100
Maximum100
Range80
Interquartile range (IQR)74.25

Descriptive statistics

Standard deviation35.01593
Coefficient of variation (CV)0.63091767
Kurtosis-1.851142
Mean55.5
Median Absolute Deviation (MAD)26
Skewness0.34314191
Sum777
Variance1226.1154
MonotonicityNot monotonic
2025-01-27T13:36:02.018188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
100 4
28.6%
23 3
21.4%
22 2
14.3%
89 1
 
7.1%
59 1
 
7.1%
20 1
 
7.1%
41 1
 
7.1%
55 1
 
7.1%
ValueCountFrequency (%)
20 1
 
7.1%
22 2
14.3%
23 3
21.4%
41 1
 
7.1%
55 1
 
7.1%
59 1
 
7.1%
89 1
 
7.1%
100 4
28.6%
ValueCountFrequency (%)
100 4
28.6%
89 1
 
7.1%
59 1
 
7.1%
55 1
 
7.1%
41 1
 
7.1%
23 3
21.4%
22 2
14.3%
20 1
 
7.1%
Distinct13
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2025-01-27T13:36:02.064987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length18.5
Mean length18.428571
Min length17

Characters and Unicode

Total characters258
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)85.7%

Sample

1st rowTopic Rank - #-171
2nd rowTopic Rank - #-9140
3rd rowTopic Rank - #-418
4th rowTopic Rank - #-1598
5th rowTopic Rank - #2023
ValueCountFrequency (%)
topic 14
25.0%
rank 14
25.0%
14
25.0%
1598 2
 
3.6%
171 1
 
1.8%
9140 1
 
1.8%
418 1
 
1.8%
2023 1
 
1.8%
1810 1
 
1.8%
2556 1
 
1.8%
Other values (6) 6
10.7%
2025-01-27T13:36:02.143054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
42
16.3%
- 23
 
8.9%
T 14
 
5.4%
a 14
 
5.4%
o 14
 
5.4%
k 14
 
5.4%
n 14
 
5.4%
# 14
 
5.4%
R 14
 
5.4%
c 14
 
5.4%
Other values (12) 81
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 258
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
42
16.3%
- 23
 
8.9%
T 14
 
5.4%
a 14
 
5.4%
o 14
 
5.4%
k 14
 
5.4%
n 14
 
5.4%
# 14
 
5.4%
R 14
 
5.4%
c 14
 
5.4%
Other values (12) 81
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 258
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
42
16.3%
- 23
 
8.9%
T 14
 
5.4%
a 14
 
5.4%
o 14
 
5.4%
k 14
 
5.4%
n 14
 
5.4%
# 14
 
5.4%
R 14
 
5.4%
c 14
 
5.4%
Other values (12) 81
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 258
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
42
16.3%
- 23
 
8.9%
T 14
 
5.4%
a 14
 
5.4%
o 14
 
5.4%
k 14
 
5.4%
n 14
 
5.4%
# 14
 
5.4%
R 14
 
5.4%
c 14
 
5.4%
Other values (12) 81
31.4%

Better Than
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160
Minimum18
Maximum395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size244.0 B
2025-01-27T13:36:02.172103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21.25
Q155.25
median133.5
Q3246.75
95-th percentile382
Maximum395
Range377
Interquartile range (IQR)191.5

Descriptive statistics

Standard deviation126.35359
Coefficient of variation (CV)0.78970996
Kurtosis-0.57182346
Mean160
Median Absolute Deviation (MAD)94.5
Skewness0.7432884
Sum2240
Variance15965.231
MonotonicityNot monotonic
2025-01-27T13:36:02.198403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
152 2
14.3%
107 1
 
7.1%
395 1
 
7.1%
115 1
 
7.1%
35 1
 
7.1%
43 1
 
7.1%
18 1
 
7.1%
375 1
 
7.1%
177 1
 
7.1%
270 1
 
7.1%
Other values (3) 3
21.4%
ValueCountFrequency (%)
18 1
7.1%
23 1
7.1%
35 1
7.1%
43 1
7.1%
92 1
7.1%
107 1
7.1%
115 1
7.1%
152 2
14.3%
177 1
7.1%
270 1
7.1%
ValueCountFrequency (%)
395 1
7.1%
375 1
7.1%
286 1
7.1%
270 1
7.1%
177 1
7.1%
152 2
14.3%
115 1
7.1%
107 1
7.1%
92 1
7.1%
43 1
7.1%
Distinct8
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2025-01-27T13:36:02.249102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length29
Mean length22.5
Min length12

Characters and Unicode

Total characters315
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)28.6%

Sample

1st rowHuman Physiology (15)
2nd rowHuman Physiology PYQ
3rd rowHuman Physiology (15)
4th rowHuman Physiology PYQ
5th rowHuman Physiology (15)
ValueCountFrequency (%)
human 9
20.0%
pyq 8
17.8%
physiology 7
15.6%
15 3
 
6.7%
health 3
 
6.7%
reproduction 2
 
4.4%
reproductive 2
 
4.4%
and 2
 
4.4%
microbes 1
 
2.2%
disease 1
 
2.2%
Other values (7) 7
15.6%
2025-01-27T13:36:02.332049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32
 
10.2%
P 19
 
6.0%
o 18
 
5.7%
H 16
 
5.1%
E 15
 
4.8%
y 14
 
4.4%
A 12
 
3.8%
R 11
 
3.5%
I 11
 
3.5%
N 9
 
2.9%
Other values (34) 158
50.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 315
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
32
 
10.2%
P 19
 
6.0%
o 18
 
5.7%
H 16
 
5.1%
E 15
 
4.8%
y 14
 
4.4%
A 12
 
3.8%
R 11
 
3.5%
I 11
 
3.5%
N 9
 
2.9%
Other values (34) 158
50.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 315
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
32
 
10.2%
P 19
 
6.0%
o 18
 
5.7%
H 16
 
5.1%
E 15
 
4.8%
y 14
 
4.4%
A 12
 
3.8%
R 11
 
3.5%
I 11
 
3.5%
N 9
 
2.9%
Other values (34) 158
50.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 315
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
32
 
10.2%
P 19
 
6.0%
o 18
 
5.7%
H 16
 
5.1%
E 15
 
4.8%
y 14
 
4.4%
A 12
 
3.8%
R 11
 
3.5%
I 11
 
3.5%
N 9
 
2.9%
Other values (34) 158
50.2%
Distinct9
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2025-01-27T13:36:02.386291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length27.5
Mean length25.714286
Min length18

Characters and Unicode

Total characters360
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)42.9%

Sample

1st rowBody Fluids and Circulation
2nd rowBody Fluids and Circulation
3rd rowBody Fluids and Circulation
4th rowBody Fluids and Circulation
5th rowBody Fluids and Circulation
ValueCountFrequency (%)
and 9
18.4%
body 6
12.2%
circulation 6
12.2%
fluids 6
12.2%
health 4
8.2%
reproductive 3
 
6.1%
human 3
 
6.1%
in 1
 
2.0%
gas 1
 
2.0%
respiration 1
 
2.0%
Other values (9) 9
18.4%
2025-01-27T13:36:02.524850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
42
 
11.7%
i 33
 
9.2%
a 30
 
8.3%
d 26
 
7.2%
n 26
 
7.2%
o 21
 
5.8%
e 21
 
5.8%
u 19
 
5.3%
l 18
 
5.0%
r 18
 
5.0%
Other values (20) 106
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
42
 
11.7%
i 33
 
9.2%
a 30
 
8.3%
d 26
 
7.2%
n 26
 
7.2%
o 21
 
5.8%
e 21
 
5.8%
u 19
 
5.3%
l 18
 
5.0%
r 18
 
5.0%
Other values (20) 106
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
42
 
11.7%
i 33
 
9.2%
a 30
 
8.3%
d 26
 
7.2%
n 26
 
7.2%
o 21
 
5.8%
e 21
 
5.8%
u 19
 
5.3%
l 18
 
5.0%
r 18
 
5.0%
Other values (20) 106
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
42
 
11.7%
i 33
 
9.2%
a 30
 
8.3%
d 26
 
7.2%
n 26
 
7.2%
o 21
 
5.8%
e 21
 
5.8%
u 19
 
5.3%
l 18
 
5.0%
r 18
 
5.0%
Other values (20) 106
29.4%

Questions Count
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.5
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size244.0 B
2025-01-27T13:36:02.550793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile21.3
Q123
median48
Q397.25
95-th percentile100
Maximum100
Range80
Interquartile range (IQR)74.25

Descriptive statistics

Standard deviation35.01593
Coefficient of variation (CV)0.63091767
Kurtosis-1.851142
Mean55.5
Median Absolute Deviation (MAD)26
Skewness0.34314191
Sum777
Variance1226.1154
MonotonicityNot monotonic
2025-01-27T13:36:02.577855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
100 4
28.6%
23 3
21.4%
22 2
14.3%
89 1
 
7.1%
59 1
 
7.1%
20 1
 
7.1%
41 1
 
7.1%
55 1
 
7.1%
ValueCountFrequency (%)
20 1
 
7.1%
22 2
14.3%
23 3
21.4%
41 1
 
7.1%
55 1
 
7.1%
59 1
 
7.1%
89 1
 
7.1%
100 4
28.6%
ValueCountFrequency (%)
100 4
28.6%
89 1
 
7.1%
59 1
 
7.1%
55 1
 
7.1%
41 1
 
7.1%
23 3
21.4%
22 2
14.3%
20 1
 
7.1%

Marks for Correct Answer
Categorical

Constant 

Distinct1
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size832.0 B
4
14 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 14
100.0%

Length

2025-01-27T13:36:02.607343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T13:36:02.624578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4 14
100.0%

Most occurring characters

ValueCountFrequency (%)
4 14
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 14
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 14
100.0%

Negative Marks
Categorical

Constant 

Distinct1
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size832.0 B
1
14 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 14
100.0%

Length

2025-01-27T13:36:02.644952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T13:36:02.662179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 14
100.0%

Most occurring characters

ValueCountFrequency (%)
1 14
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 14
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 14
100.0%

Percentage Score
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.763745
Minimum5.0847458
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size244.0 B
2025-01-27T13:36:02.679481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.0847458
5-th percentile5.679661
Q114.336057
median34.065217
Q362.583149
95-th percentile96.75
Maximum100
Range94.915254
Interquartile range (IQR)48.247091

Descriptive statistics

Standard deviation31.914383
Coefficient of variation (CV)0.78291097
Kurtosis-0.58689957
Mean40.763745
Median Absolute Deviation (MAD)23.94724
Skewness0.73583836
Sum570.69244
Variance1018.5279
MonotonicityNot monotonic
2025-01-27T13:36:02.703278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
39.13043478 2
14.3%
27 1
 
7.1%
100 1
 
7.1%
29 1
 
7.1%
9 1
 
7.1%
11.23595506 1
 
7.1%
5.084745763 1
 
7.1%
95 1
 
7.1%
45.45454545 1
 
7.1%
68.29268293 1
 
7.1%
Other values (3) 3
21.4%
ValueCountFrequency (%)
5.084745763 1
7.1%
6 1
7.1%
9 1
7.1%
11.23595506 1
7.1%
23.63636364 1
7.1%
27 1
7.1%
29 1
7.1%
39.13043478 2
14.3%
45.45454545 1
7.1%
68.29268293 1
7.1%
ValueCountFrequency (%)
100 1
7.1%
95 1
7.1%
72.72727273 1
7.1%
68.29268293 1
7.1%
45.45454545 1
7.1%
39.13043478 2
14.3%
29 1
7.1%
27 1
7.1%
23.63636364 1
7.1%
11.23595506 1
7.1%

Difficulty Score
Real number (ℝ)

High correlation  Unique 

Distinct14
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.953301
Minimum7.9423729
Maximum129.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size244.0 B
2025-01-27T13:36:02.727324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7.9423729
5-th percentile8.3698305
Q121.868029
median44.332609
Q381.341574
95-th percentile125.175
Maximum129.4
Range121.45763
Interquartile range (IQR)59.473546

Descriptive statistics

Standard deviation40.43757
Coefficient of variation (CV)0.74949205
Kurtosis-0.54286524
Mean53.953301
Median Absolute Deviation (MAD)27.47362
Skewness0.75503124
Sum755.34622
Variance1635.1971
MonotonicityNot monotonic
2025-01-27T13:36:02.753630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
35.9 1
 
7.1%
129.4 1
 
7.1%
38.1 1
 
7.1%
50.56521739 1
 
7.1%
15.9 1
 
7.1%
17.81797753 1
 
7.1%
52.16521739 1
 
7.1%
7.942372881 1
 
7.1%
122.9 1
 
7.1%
58.52727273 1
 
7.1%
Other values (4) 4
28.6%
ValueCountFrequency (%)
7.942372881 1
7.1%
8.6 1
7.1%
15.9 1
7.1%
17.81797753 1
7.1%
34.01818182 1
7.1%
35.9 1
7.1%
38.1 1
7.1%
50.56521739 1
7.1%
52.16521739 1
7.1%
58.52727273 1
7.1%
ValueCountFrequency (%)
129.4 1
7.1%
122.9 1
7.1%
94.56363636 1
7.1%
88.94634146 1
7.1%
58.52727273 1
7.1%
52.16521739 1
7.1%
50.56521739 1
7.1%
38.1 1
7.1%
35.9 1
7.1%
34.01818182 1
7.1%

Difficulty Level
Categorical

High correlation 

Distinct3
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Memory size880.0 B
Easy
Hard
Medium

Length

Max length6
Median length4
Mean length4.4285714
Min length4

Characters and Unicode

Total characters62
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEasy
2nd rowHard
3rd rowEasy
4th rowMedium
5th rowEasy

Common Values

ValueCountFrequency (%)
Easy 7
50.0%
Hard 4
28.6%
Medium 3
21.4%

Length

2025-01-27T13:36:02.785861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-27T13:36:02.806921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
easy 7
50.0%
hard 4
28.6%
medium 3
21.4%

Most occurring characters

ValueCountFrequency (%)
a 11
17.7%
E 7
11.3%
s 7
11.3%
y 7
11.3%
d 7
11.3%
H 4
 
6.5%
r 4
 
6.5%
M 3
 
4.8%
e 3
 
4.8%
i 3
 
4.8%
Other values (2) 6
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11
17.7%
E 7
11.3%
s 7
11.3%
y 7
11.3%
d 7
11.3%
H 4
 
6.5%
r 4
 
6.5%
M 3
 
4.8%
e 3
 
4.8%
i 3
 
4.8%
Other values (2) 6
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11
17.7%
E 7
11.3%
s 7
11.3%
y 7
11.3%
d 7
11.3%
H 4
 
6.5%
r 4
 
6.5%
M 3
 
4.8%
e 3
 
4.8%
i 3
 
4.8%
Other values (2) 6
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11
17.7%
E 7
11.3%
s 7
11.3%
y 7
11.3%
d 7
11.3%
H 4
 
6.5%
r 4
 
6.5%
M 3
 
4.8%
e 3
 
4.8%
i 3
 
4.8%
Other values (2) 6
9.7%

Interactions

2025-01-27T13:36:01.026206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:57.802941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.177505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.464516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.753169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.125042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.415304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.739662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.107532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.423926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.742200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:01.055359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:57.841345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.205619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.493277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.783602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.153221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.446813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.770080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.138195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.455067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.770281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:01.134668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:57.868928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.228901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.517317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.811493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.176926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.474847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.797965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.164719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.481248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.793505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:01.158256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:57.896482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.253444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.541701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.839014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.202598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.502083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.824522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.192965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.509250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.816061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:01.186865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:57.927786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.281064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.569104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.869215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.230353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.532630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.855478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.222685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.539644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.845013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:01.211342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:57.955999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.305972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.593863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.896494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.254947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.560681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.882672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.250560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.567507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.869586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:01.240746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:57.987080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.333908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.622050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.927152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.281944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.590656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.913822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.281178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.597247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.897157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:01.267263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.018039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.360776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.649836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.957035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.310010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.621028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.942649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.311241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.627531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.923644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:01.295038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.048316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.388547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.675758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.987944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.338388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.652526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.972022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.340578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.658732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.952028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:01.322337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.123221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.415805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.703934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.018241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.365613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.684095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.000984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.369570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.687230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.978723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:01.347677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.150119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.440346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:58.727275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.097146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.389788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:35:59.710683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.081075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.396036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:00.713511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-27T13:36:01.002530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-01-27T13:36:02.830410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Better ThanCorrect AnswersDifficulty LevelDifficulty ScoreIncorrect AnswersNegative ScorePercentage ScoreQuestions CountQuiz IDScoreSubmission IDTotal Questions
Better Than1.0000.5910.7390.999-0.708-0.7081.000-0.776-0.5580.5910.075-0.776
Correct Answers0.5911.0000.2300.590-0.435-0.4350.591-0.0660.2001.0000.276-0.066
Difficulty Level0.7390.2301.0000.7980.0000.0000.7390.5490.5500.2300.0000.549
Difficulty Score0.9990.5900.7981.000-0.693-0.6930.999-0.776-0.5570.5900.068-0.776
Incorrect Answers-0.708-0.4350.000-0.6931.0001.000-0.7080.5160.520-0.435-0.1600.516
Negative Score-0.708-0.4350.000-0.6931.0001.000-0.7080.5160.520-0.435-0.1600.516
Percentage Score1.0000.5910.7390.999-0.708-0.7081.000-0.776-0.5580.5910.075-0.776
Questions Count-0.776-0.0660.549-0.7760.5160.516-0.7761.0000.673-0.0660.2591.000
Quiz ID-0.5580.2000.550-0.5570.5200.520-0.5580.6731.0000.200-0.0950.673
Score0.5911.0000.2300.590-0.435-0.4350.591-0.0660.2001.0000.276-0.066
Submission ID0.0750.2760.0000.068-0.160-0.1600.0750.259-0.0950.2761.0000.259
Total Questions-0.776-0.0660.549-0.7760.5160.516-0.7761.0000.673-0.0660.2591.000

Missing values

2025-01-27T13:36:01.394985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-27T13:36:01.448254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Submission IDQuiz IDUser IDScoreAccuracyNegative ScoreCorrect AnswersIncorrect AnswersTotal QuestionsRank TextBetter ThanQuiz TitleQuiz TopicQuestions CountMarks for Correct AnswerNegative MarksPercentage ScoreDifficulty ScoreDifficulty Level
033649751YcDFSO4ZukTJnnFMgRNVwZTE4j4210890%3273100Topic Rank - #-171107Human Physiology (15)Body Fluids and Circulation1004127.00000035.900000Easy
13364486YcDFSO4ZukTJnnFMgRNVwZTE4j4292100%023023Topic Rank - #-9140395Human Physiology PYQBody Fluids and Circulation2341100.000000129.400000Hard
233333051YcDFSO4ZukTJnnFMgRNVwZTE4j4211696%1291100Topic Rank - #-418115Human Physiology (15)Body Fluids and Circulation1004129.00000038.100000Easy
33332426YcDFSO4ZukTJnnFMgRNVwZTE4j423690%19123Topic Rank - #-1598152Human Physiology PYQBody Fluids and Circulation234139.13043550.565217Medium
432950451YcDFSO4ZukTJnnFMgRNVwZTE4j423631%20920100Topic Rank - #202335Human Physiology (15)Body Fluids and Circulation100419.00000015.900000Easy
532848857YcDFSO4ZukTJnnFMgRNVwZTE4j424038%16101689Topic Rank - #181043ReproductionHuman Reproduction894111.23595517.817978Easy
63284146YcDFSO4ZukTJnnFMgRNVwZTE4j423650%99923Topic Rank - #-1598152Human Physiology PYQBody Fluids and Circulation234139.13043552.165217Medium
732151420YcDFSO4ZukTJnnFMgRNVwZTE4j421230%73759Topic Rank - #255618PRINCIPLES OF INHERITANCE AND VARIATION PYQprinciples of inheritance and variation59415.0847467.942373Easy
832096324YcDFSO4ZukTJnnFMgRNVwZTE4j4276100%019020Topic Rank - #-8479375MICROBES IN HUMAN WELFARE PYQmicrobes in human welfare204195.000000122.900000Hard
932091618YcDFSO4ZukTJnnFMgRNVwZTE4j4240100%010022Topic Rank - #-2380177REPRODUCTIVE HEALTH PYQreproductive health224145.45454558.527273Medium
Submission IDQuiz IDUser IDScoreAccuracyNegative ScoreCorrect AnswersIncorrect AnswersTotal QuestionsRank TextBetter ThanQuiz TitleQuiz TopicQuestions CountMarks for Correct AnswerNegative MarksPercentage ScoreDifficulty ScoreDifficulty Level
432950451YcDFSO4ZukTJnnFMgRNVwZTE4j423631%20920100Topic Rank - #202335Human Physiology (15)Body Fluids and Circulation100419.00000015.900000Easy
532848857YcDFSO4ZukTJnnFMgRNVwZTE4j424038%16101689Topic Rank - #181043ReproductionHuman Reproduction894111.23595517.817978Easy
63284146YcDFSO4ZukTJnnFMgRNVwZTE4j423650%99923Topic Rank - #-1598152Human Physiology PYQBody Fluids and Circulation234139.13043552.165217Medium
732151420YcDFSO4ZukTJnnFMgRNVwZTE4j421230%73759Topic Rank - #255618PRINCIPLES OF INHERITANCE AND VARIATION PYQprinciples of inheritance and variation59415.0847467.942373Easy
832096324YcDFSO4ZukTJnnFMgRNVwZTE4j4276100%019020Topic Rank - #-8479375MICROBES IN HUMAN WELFARE PYQmicrobes in human welfare204195.000000122.900000Hard
932091618YcDFSO4ZukTJnnFMgRNVwZTE4j4240100%010022Topic Rank - #-2380177REPRODUCTIVE HEALTH PYQreproductive health224145.45454558.527273Medium
1031517925YcDFSO4ZukTJnnFMgRNVwZTE4j4211293%228241Topic Rank - #-5215270HUMAN HEALTH AND DISEASE PYQhuman health and disease414168.29268388.946341Hard
1131508118YcDFSO4ZukTJnnFMgRNVwZTE4j426484%316322Topic Rank - #-5764286REPRODUCTIVE HEALTH PYQreproductive health224172.72727394.563636Hard
1225777458YcDFSO4ZukTJnnFMgRNVwZTE4j425243%17131755Topic Rank - #30192ReproductionReproductive Health554123.63636434.018182Easy
1319580850YcDFSO4ZukTJnnFMgRNVwZTE4j422466%363100Topic Rank - #239223Human Physiology (14)Respiration and Gas Exchange100416.0000008.600000Easy